摘要
为解决泵站主机组真机试验困难,无法获取动力特性方程的技术难题,提出一种通过制造厂家提供的模型运转综合特性曲线获取泵站主机组真机动力特性方程的方法。首先,通过模型运转综合特性曲线获取离散特性数据,对其进行二乘法拟合形成泵站主机组模型动力特性方程;其次,通过真机计算机监控历史数据库和泵站运行管理云端数据库,获取静态运行数据样本,采用3次指数矩阵数据处理方法进行静态点修正,初步获得泵站主机组真机动力特性方程;最后,将可拓理论与神经网络计算方法相结合,创新一种可拓神经网络训练方法,实现对初始泵站主机组真机动力特性方程的动态修正,且该方法具有自适应性、自学习性和可拓性等特点。应用实践表明,三次指数矩阵数据处理方法和可拓神经网络训练方法的创新性结合运用,能够较精确、有效、可靠地获取泵站主机组真机动力特性方程,为整个泵站的安全可靠运行、主机组组合调度和负载优化分配等提供了科学决策依据。
In view of the technical problem that it is difficult to obtain the dynamic characteristic equation of the main engine unit of most pumping stations in China,a method is proposed to obtain the dynamic characteristic equation of the main unit of the pumping station through the comprehensive characteristic curve of the model operation provided by the manufacturer of the main unit of the pumping station.Firstly,the discrete characteristic data is obtained through the comprehensive characteristic curve of the model operation,and then the dynamic characteristic equation of the main unit model of the pumping station is formed by the double multiplication fitting.Secondly,the static operation data samples are obtained through the real computer monitoring historical database and the cloud database of the pump station operation management,and the static point correction is carried out by using the cubic index matrix data processing method.Finally,the extension theory and neural network calculation method are combined to create an extension neural network training method to realize the dynamic correction of the dynamic characteristic equation of the original main unit of the pumping station,and the method has the characteristics of self adaptability,self-learning habit and extension.The application practice shows that the innovative combination of the cubic index matrix data processing method and the extension neural network training method can accurately,effectively and reliably obtain the dynamic characteristic equation of the main unit of the pumping station,which provides a scientific decision-making basis for the safe and reliable operation of the whole pumping station,the combined dispatching of the main units and the optimal load distribution.
作者
杨玉泉
张仁贡
YANG Yu-quan;ZHANG Ren-gong(Zhejiang Tongji Vocational College of Science and Technology,Hangzhou 311231,China;Zhejiang Yugong Information Technology Co.,Ltd.Hangzhou 310009,China;Zhejiang University of Technology,Hangzhou 310014,China)
出处
《中国农村水利水电》
北大核心
2022年第1期105-110,共6页
China Rural Water and Hydropower
基金
国家自然科学基金(60874074)
浙江省教育厅科研项目(Y201942951)
浙江省水利厅科技项目(RC1981)。
关键词
动力特性
安全运行
可拓神经网络
排涝泵站
主机组
大数据
dynamic characteristics
safety operation
extension neural network
drainage pumping station
host group
big data
